> > What do you suggest in order to assign a new observation to a determined > cluster? > As I mentioned already, I would simply assign the new observation to the cluster to whose exemplar the new observation is most similar to (in a knn1-like fashion). To compute these similarities, you can use the daisy() function. However, you have to do some tricks, since daisy() is designed for computing square matrices of all mutual distances for a given data set. I did not find another function that is better suitable (e.g. a function that allows to compute simply the distance of two distinct samples). Maybe others have an idea. In any case, you have to make sure that data either remain unscaled or that you take care yourself that your new observation is scaled exactly with the same parameters that were used for clustering before.
Cheers, Ulrich -- View this message in context: http://r.789695.n4.nabble.com/cluster-analysis-and-supervised-classification-an-alternative-to-knn1-tp2231656p2233308.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
